Geo-clustered chronic affinity: pathways from socio-economic disadvantages to health disparities.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:31984364", "kind": "science-record", "revision": 6, "content_hash": "sha256:13206545f7faeaebfdfd28b848881eb76c753c4dcf07d725b2a3ca0102686cac", "created_at": "2026-11-01T15:40:55Z", "updated_at": "2026-11-01T21:22:50Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Geo-clustered chronic affinity: pathways from socio-economic disadvantages to health disparities.", "doi": "10.1093/jamiaopen/ooz029", "pmid": "31984364", "authors": ["Eun Kyong Shin", "Youngsang Kwon", "Arash Shaban-Nejad"], "journal": "JAMIA open", "year": 2019, "abstract": "OBJECTIVE: Our objective was to develop and test a new concept (affinity) analogous to multimorbidity of chronic conditions for individuals at census tract level in Memphis, TN. The use of affinity will improve the surveillance of multiple chronic conditions and facilitate the design of effective interventions.\nMETHODS: We used publicly available chronic condition data (Center for Disease Control and Prevention 500 Cities project), socio-demographic data (US Census Bureau), and demographics data (Environmental Systems Research Institute). We examined the geographic pattern of the affinity of chronic conditions using global Moran's I and Getis-Ord Gi* statistics and its association with socio-economic disadvantage (poverty, unemployment, and crime) using robust regression models. We also used the most common behavioral factor, smoking, and other demographic factors (percent of the male population, percent of the population 67 years, and over and total population size) as control variables in the model.\nRESULTS: A geo-distinctive pattern of clustered chronic affinity associated with socio-economic deprivation was observed. Statistical results confirmed that neighborhoods with higher rates of crime, poverty, and unemployment were associated with an increased likelihood of having a higher affinity among major chronic conditions. With the inclusion of smoking in the model, however, only the crime prevalence was statistically significantly associated with the chronic affinity.\nCONCLUSION: Chronic affinity disadvantages were disproportionately accumulated in socially disadvantaged areas. We showed links between commonly co-observed chronic diseases at the population level and systematically explored the complexity of affinity and socio-economic disparities. Our affinity score, based on publicly available datasets, served as a surrogate for multimorbidity at the population level, which may assist policymakers and public health planners to identify urgent hot spots for chronic disease and allocate clinical, medical and healthcare resources efficiently.", "claims": [{"id": "claim-method", "statement": "Next, using robust regression analyses, we tested if there is any statistically significant association between socio-economic disadvantages—crime, poverty and unemployment—and affinity prevalence at the census tract level (N = 178).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:31984364", "locator": "sections[2] \"DATA AND METHODS\" paragraph 5", "quote": "Next, using robust regression analyses, we tested if there is any statistically significant association between socio-economic disadvantages—crime, poverty and unemployment—and affinity prevalence at the census tract level (N = 178)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Chronic conditions and smoking prevalence data were taken from the CDC 500 cities dataset (2015).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:31984364", "locator": "sections[2] \"DATA AND METHODS\" paragraph 2", "quote": "Chronic conditions and smoking prevalence data were taken from the CDC 500 cities dataset (2015)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": null, "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:31984364"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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